A Predictive Model for 30-Day Mortality of Fungemia in ICUs

0Citations
Citations of this article
18Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background: Few predictive models have been established to predict the risk of 30-day mortality from fungemia. This study aims to create a nomogram to predict the 30-day mortality of fungemia in ICUs. Methods: Data of ICU patients with fungemia from both the Medical Information Mart for Intensive Care (MIMIC-III) database and the Grade-III Class-A hospital in China were collected. The data extracted from the MIMIC-III database functioned as the training dataset, which was used to construct a predictive model for 30-day mortality risk in ICU patients with fungemia; the data from the hospital functioned as the validation dataset, which was used to validate the model. A predictive model for 30-day mortality risk in ICU patients with fungemia was then built based on R software. Such indicators as C-index and calibration curve were utilized to evaluate the prediction ability of the model. Data of ICU patients with fungemia from the hospital were used as a validation dataset to validate the model. Results: Predictive models were constructed by age, international normalized ratio (INR), renal failure, liver disease, respiratory rate (RR), glucocorticoid therapy, antifungal therapy, and platelets. The C-index value of the models was 0.838 (95% CI: 0.79096– 0.88504). Attested by external validation results, the model has satisfactory predictive ability. Conclusion: The 30-day mortality risk predictive model for ICU patients with fungemia constructed in this study has good predictive ability and may hopefully provide a 30-day mortality risk screening tool for ICU patients with fungemia.

Author supplied keywords

Cite

CITATION STYLE

APA

Xie, P., Wang, W., & Dong, M. (2022). A Predictive Model for 30-Day Mortality of Fungemia in ICUs. Infection and Drug Resistance, 15, 7841–7852. https://doi.org/10.2147/IDR.S389161

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free